Outline of Main Results

Some of the novel results herein may be of interest to
theoretical computer scientists and mathematicians (Sections
2-6), some to researchers
in the fields of machine learning and inductive inference
(the science of making predictions based on observations,
e.g., 6-7), some to
physicists (e.g., 6-8), some to
philosophers (e.g., 7-8). Sections
7-8 might help those usually uninterested
in technical details to decide whether they would also like to delve
into the more formal Sections 2-6.
In what follows, we summarize the main contributions and
provide pointers to the most important theorems.

Section 2 introduces universal Turing Machines
(TMs) more general than those considered in previous related work:
unlike traditional TMs, General TMs or GTMs may edit their
previous outputs (compare inductive TMs [#!Burgin:83!#]),
and Enumerable Output Machines (EOMs) may do
this provided the output does not decrease lexicographically. We will
define: a formally describable object x has a finite, never halting
GTM program that computes x such that each output bit is revised at
most finitely many times; that is, each finite prefix of x eventually
stabilizes (Defs. 2.1-2.5);
describable functions can be implemented by such programs
(Def. 2.10); weakly decidable problems have
solutions computable by never halting programs whose output is wrong for
at most finitely many steps (Def. 2.11). Theorem 2.1
generalizes the halting problem by demonstrating that it is not weakly
decidable whether a finite string is a description of a describable
object (compare a related result for analytic TMs by Hotz, Vierke and
Schieffer [#!Hotz:95!#]).

Section 3 generalizes the traditional
concept of Kolmogorov complexity or algorithmic information
[#!Kolmogorov:65!#,#!Solomonoff:64!#,#!Chaitin:69!#] of finite x (the
length of the shortest halting program computing x) to the case
of objects describable by nonhalting programs on EOMs and GTMs
(Defs. 3.2-3.4). It is shown that
the generalization for EOMs is describable, but the one for GTMs is
not (Theorem 3.1). Certain objects are much more compactly
encodable on EOMs than on traditional monotone TMs, and Theorem
3.3 shows that there are also objects with short GTM descriptions
yet incompressible on EOMs and therefore ``more random'' than Chaitin's
[#!Chaitin:87!#], the halting probability of a TM
with random input, which is incompressible only on monotone TMs.
This yields a natural TM type-specific complexity hierarchy expressed
by Inequality (14).

Section 4 discusses probability distributions on
describable objects as well as the nondescribable convergence
probability of a GTM (Def. 4.14). It also introduces
describable (semi)measures as well as cumulatively enumerable
measures (CEMs, Def. 4.5), where the cumulative probability
of all strings lexicographically greater than a given string x
is EOM-computable or enumerable. Theorem 4.1 shows
that there is a universal CEM that dominates all other CEMs, in the
sense that it assigns higher probability to any finite y, save for
a constant factor independent of y. This probability is shown to be
equivalent to the probability that an EOM whose input bits are chosen
randomly produces an output starting with y (Corollary 4.3
and Lemma 4.2). The nonenumerable universal CEM also dominates
enumerable priors studied in previous work by Solomonoff, Levin and others
[#!Solomonoff:64!#,#!Zvonkin:70!#,#!Levin:74!#,#!Gacs:74!#,#!Chaitin:75!#,#!Gacs:83!#,#!Schnorr:73!#,#!Solomonoff:78!#,#!Chaitin:87!#,#!LiVitanyi:97!#].
Theorem 4.2 shows that there is no universal approximable
measure (proof by M. Hutter).

Section 5 establishes relationships between generalized
Kolmogorov complexity and generalized algorithmic probability, extending
previous work on enumerable semimeasures by Levin, Gács, and others
[#!Zvonkin:70!#,#!Levin:74!#,#!Gacs:74!#,#!Chaitin:75!#,#!Gacs:83!#,#!LiVitanyi:97!#].
For instance, Theorem 5.3 shows that the universal CEM assigns
a probability to each enumerable object proportional to
raised to the power of the length of its minimal EOM-based description,
times a small corrective factor. Similarly, objects with approximable
probabilities yet without very short descriptions on GTMs are necessarily
very unlikely a priori (Theorems 5.4 and 5.5).
Additional suspected links between generalized Kolmogorov complexity and
probability are expressed in form of Conjectures 5.1-5.3.

Section 6 addresses issues of temporal complexity
ignored in the previous sections on describable universe histories
(whose computation may require excessive time without recursive
bounds). In Subsection 6.2, Levin's universal search algorithm
[#!Levin:73!#,#!Levin:84!#] (which takes into account program runtime in
an optimal fashion) is modified to obtain the fastest way of computing
all ``S-describable'' universes computable within countable time
(Def. 6.1, Section 6.3); uncountably
many other universes are ignored because they do not even exist
from a constructive point of view. Postulate 6.1 then
introduces a natural resource-oriented bias reflecting constraints
of whoever calculated our universe (possibly as a by-product of a
search for something else): we assign to universes prior probabilities
inversely proportional to the time and space resources consumed by
the most efficient way of computing them. Given the resulting ``speed
prior S'' (Def. 6.5) and past observations x, Theorem
6.1 and Corollary 6.1 demonstrate that the
best way of predicting a future y is to minimize the Levin complexity
of (x,y).

Section 7 puts into perspective the
algorithmic priors (recursive and enumerable) introduced in
previous work on inductive inference by Solomonoff and others
[#!Solomonoff:64!#,#!Solomonoff:78!#,#!LiVitanyi:97!#,#!Hutter:99!#], as well
as the novel priors discussed in the present paper (cumulatively
enumerable, approximable, resource-optimal). Collectively they yield an
entire spectrum of algorithmic TOEs. We evaluate
the plausibility of each prior
being the one from which our own universe is sampled, discuss
its connection to ``Occam's razor'' as well as certain physical and
philosophical consequences, argue that the resource-optimal speed
prior S may be the most plausible one (Section 7.4),
analyze the inference problem from the point of view of an observer
[#!Boskovich:1755!#,#!Boskovich:1966!#,#!Toffoli:79!#,#!Zurek:91!#,#!Svozil:94!#,#!Roessler:98!#]
evolving in a universe sampled from S, make appropriate predictions
for our own universe (Section 7.5), and discuss their
falsifiability.